Agenda

  • Introductions
  • Stat major?
  • Super brief look at Canvas / Syllabus
  • Maybe we see a little course material

Statistics

problem

\(\downarrow\)

experimental design

\(\downarrow\)

data collection

\(\downarrow\)

data analysis

\(\downarrow\)

conclusions

Before the Linear Model: Exploratory Analyses

  • data cleaning
  • summary statistics
  • graphical summaries

Regression

Broadly, this class is about regression.

What is Regression?

Broadly, regression is when we model the relationship between a variable \(Y\) and some set of variables \(X_1, \dots, X_p\).

  • \(Y\) is called the response, independent, or explanatory variable.
  • \(X_1, \dots, X_p\) are the predictor, input, independent, or explanatory variables.

What is Regression?

Regression analyses have several possible objectives, most commonly:

  • prediction of future observations
  • examination of relationships between explanatory variables and response
  • generating a description of data structure